#%%
class Perceptron():
    def __init__(self,input_num,activator):
        '''
        '''
        self.activator=activator
        self.weights=[0.0 for _ in range(input_num)]
        self.bias=0.0

    def __str__(self):
        '''
        '''
        return 'weights \t:%s \n bias \t:%f\n' %(self.weights,self.bias)
            
    def predict(self,input_vec):
        '''
        '''
        # 把input_vec[x1,x2,x3...]和weights[w1,w2,w3,...]打包在一起
        # 变成[(x1,w1),(x2,w2),(x3,w3),...]
        # 然后利用map函数计算[x1*w1, x2*w2, x3*w3]
        # 最后利用sum求和
        zipped = list(zip(input_vec, self.weights))
        sum_total = sum(list(map(lambda x_y: x_y[0] * x_y[1], zipped)))
        return self.activator(sum_total + self.bias)

    def train(self, input_vecs, labels, iteration, rate):
        '''
        输入训练数据：一组向量、与每个向量对应的label；以及训练轮数、学习率
        '''
        for i in range(iteration):
            self._one_iteration(input_vecs, labels, rate)
            
    def _one_iteration(self, input_vecs, labels, rate):
        '''
        一次迭代，把所有的训练数据过一遍
        '''
        # 把输入和输出打包在一起，成为样本的列表[(input_vec, label), ...]
        # 而每个训练样本是(input_vec, label)
        samples = zip(input_vecs, labels)
        # 对每个样本，按照感知器规则更新权重
        for (input_vec, label) in samples:
            # 计算感知器在当前权重下的输出
            output = self.predict(input_vec)
            # 更新权重
            self._update_weights(input_vec, output, label, rate)
            
    def _update_weights(self, input_vec, output, label, rate):
        '''
        按照感知器规则更新权重
        '''
        # 把input_vec[x1,x2,x3,...]和weights[w1,w2,w3,...]打包在一起
        # 变成[(x1,w1),(x2,w2),(x3,w3),...]
        # 然后利用感知器规则更新权重
        
        #self.weights = map(lambda (x, w): w + rate * delta * x,zip(input_vec, self.weights))
        print(input_vec, output, label, "rate", rate)
        delta = label - output
        self.weights = list(map(
            lambda x_w: rate * delta * x_w[0] + x_w[1],
            zip(input_vec, self.weights)))
        # 更新bias
        self.bias += rate * delta

def f_active_function(x):
    '''
    定义激活函数f
    '''
    return 1 if x > 0 else 0


def get_training_dataset():
    '''
    基于and真值表构建训练数据
    '''
    # 构建训练数据
    # 输入向量列表
    input_vecs = [[1, 1], [0, 0], [1, 0], [0, 1]]
    # 期望的输出列表，注意要与输入一一对应
    # [1,1] -> 1, [0,0] -> 0, [1,0] -> 0, [0,1] -> 0
    labels = [1, 0, 1, 1]
    return input_vecs, labels


def train_and_perceptron():
    '''
    使用and真值表训练感知器
    '''
    # 创建感知器，输入参数个数为2（因为and是二元函数），激活函数为f
    p = Perceptron(2, f_active_function)
    # 训练，迭代10轮, 学习速率为0.1
    input_vecs, labels = get_training_dataset()
    p.train(input_vecs, labels, 10, 0.1)
    # 返回训练好的感知器
    return p


if __name__ == '__main__':
    # 训练and感知器
    and_perception = train_and_perceptron()
    print(and_perception)
    print("input value{0}, predict:{1}".format([0, 0], and_perception.predict([0, 0])))
    print(and_perception.predict([1, 0]))
    print(and_perception.predict([0, 1]))
    print(and_perception.predict([1, 1]))
#%%
from functools import reduce 
input_vecs = [[1, 1], [0, 0], [1, 0], [0, 1]]
    # 期望的输出列表，注意要与输入一一对应
    # [1,1] -> 1, [0,0] -> 0, [1,0] -> 0, [0,1] -> 0
labels = [1, 0, 1, 1]
samples = zip(input_vecs, labels)
print(samples)
zipped = list(zip(input_vecs, labels))
print(zipped)
weights=[0.0 for _ in range(2)]
print(weights)
g=lambda x:x**2
print(g(4),g(5))
foo = [2, 18, 9, 22, 17, 24, 8, 12, 27]
print(list(filter(lambda x:x%3==0,foo)))
print(list(map(lambda x:x*2,foo)))

print (reduce(lambda x, y: x + y, foo))

sum_total = list(map(lambda x_y: x_y[0] * x_y[1], zipped))
print(sum_total)
input_num=2
weights=[0.0 for _ in range(input_num)]
print(weights)
zipped = list(zip(input_vecs, weights))
print(weights)
sum_total = sum(list(map(lambda x_y: x_y[0] * x_y[1], zipped)))
print(sum_total)
#%%
def train( input_vecs, labels, iteration, rate):
        '''
        输入训练数据：一组向量、与每个向量对应的label；以及训练轮数、学习率
        '''
        for i in range(iteration):
            _one_iteration(input_vecs, labels, rate)
def _one_iteration( input_vecs, labels, rate):
        '''
        一次迭代，把所有的训练数据过一遍
        '''
        # 把输入和输出打包在一起，成为样本的列表[(input_vec, label), ...]
        # 而每个训练样本是(input_vec, label)
        samples = zip(input_vecs, labels)
        # 对每个样本，按照感知器规则更新权重
        for (input_vec, label) in samples:
            # 计算感知器在当前权重下的输出
            output = predict(input_vec)
            # 更新权重
            _update_weights(input_vec, output, label, rate)
def _update_weights(input_vec, output, label, rate):
        '''
        按照感知器规则更新权重
        '''
        # 把input_vec[x1,x2,x3,...]和weights[w1,w2,w3,...]打包在一起
        # 变成[(x1,w1),(x2,w2),(x3,w3),...]
        # 然后利用感知器规则更新权重
       
        #self.weights = map(lambda (x, w): w + rate * delta * x,zip(input_vec, self.weights))
        print(input_vec, output, label, "rate", rate)
        delta = label - output
        weights = list(map(
            lambda x_w: rate * delta * x_w[0] + x_w[1],
            zip(input_vec, weights)))
        # 更新bias
        bias += rate * delta

zipped = list(zip(input_vecs, labels))
print(zipped)
bias=0.0
def predict(input_vec):
        '''
        '''
        # 把input_vec[x1,x2,x3...]和weights[w1,w2,w3,...]打包在一起
        # 变成[(x1,w1),(x2,w2),(x3,w3),...]
        # 然后利用map函数计算[x1*w1, x2*w2, x3*w3]
        # 最后利用sum求和
        zipped = list(zip(input_vec, weights))
        sum_total = sum(list(map(lambda x_y: x_y[0] * x_y[1], zipped)))
        return activator(sum_total + bias)
def activator(x):
    '''
    定义激活函数f
    '''
    return 1 if x > 0 else 0
#%%
input_num=2
weights=[0.0 for _ in range(input_num)]
print(weights)
input_vecs = [[1, 1], [0, 0], [1, 0], [0, 1]]
    # 期望的输出列表，注意要与输入一一对应
    # [1,1] -> 1, [0,0] -> 0, [1,0] -> 0, [0,1] -> 0
labels = [1, 0, 1, 1]
samples = zip(input_vecs, labels)
print(samples)
zipped = list(zip(input_vecs, labels))
print(zipped)
input_vec=[1,0] 
print(list(zip(input_vec, weights)))
samples = zip(input_vecs, labels)
for (input_vec, label) in samples:
            # 计算感知器在当前权重下的输出
            output = predict(input_vec)
            print(output)
